Genetic Analysis of Generalized S-Transform

Yun Lin,Xiaowan Yu,Chunguang Ma,Zheng Dou,Zhiqiang Wu,Zhiping Zhang 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.4

This text starts with the short time Fourier transform and continuous wavelet transform to deduce the generalized S-transformation. From the point of generation views, we analyzed a relative relationship between generalized S-transformation and the short time Fourier transform, and the other relative relationship between generalized S transform and continuous wavelet transform. The article gives the definition of “the gene mutation of formula” and “the genetic restructuring of formula”, and introduces the deriving process of the two core concept. Theoretical analyses show that generalized S-transformation inherited the desirable characteristics in short time Fourier transform which use the window function to select suitable signal. Through genome sequencing of specific parameters, generalized S-transformation has a stronger adaptation that the time-frequency window could make real-time adjusting of frequency. Moreover, generalized S-transformation breaks out limitation that the wavelet function has to content the admissible conditions. From the point of gene mutation, we give the definition of “the gene mutation of formula”. Based on the structure form of wavelet functions, we define the generalized S-transformation with a wider domain of definition. Generalized S-transformation inherited the desirable characteristics of the short time Fourier transform and continuous wavelet transform. It has great utility and flexibility in analyzing non-stationary signals.

부분 푸리에 영역과 선형 시간-주파수 분포의 옮김 불변 특성

두락루트피에,강현구,윤석호,이주미,권형문,최상원,송익호,Durak Lutfiye,Kang Hyun Gu,Yoon Seokho,Lee Jumi,Kwon Hyoungmoon,Choi Sang Won,Song Iickho 한국통신학회 2005 韓國通信學會論文誌 Vol.30 No.11c

시간 영역과 주파수 영역을 사이의 공간을 뜻하는 부분 푸리에 영역으로 (fractional Fourier domains) 선형 시간-주파수 분포의 옮김 불변 특성을 일반화한다. 다른 선형 사주파수 분포와 달리 짧은 시간 푸리에 변환은(short time Fourier transform: STFT) 부분 푸리에 영역에서 크기 (magnitude-wise) 옮김 불변을 지니는데, 이 짧은 시간 푸리에 변환을 쓰면 분포를 좀더 쉽게 해석할 수 있다. 특히, 부분 푸리에 영역에서 크기 옮김 불변인 선형 분포는 짧은 시간 푸리에 변환뿐이라는 것을 밝힌다. In this paper, we generalize the shift-invariance properties of linear time-frequency distributions to the fractional Fourier domains that interpolate between the time and frequency domains. Magnitude-wise shift invariance in arbitrary fractional Fourier domains distinguishes the short-time Fourier transform (STFT) among all linear time-frequency distributions and simplifies the interpretation of the resultant distribution. We prove that the STFT is the only linear distribution that satisfies the magnitude-wise shift-invariance property in the fractional Fourier domains.

Time-frequency Analysis Based on the S-transform

Lin Yun,Xu Xiaochun,Li Bin,Pang Jinfeng 보안공학연구지원센터(IJSIP) 2013 International Journal of Signal Processing, Image Vol.6 No.5

Detection of Apnea Signal using UWB Radar based on Short-Time-Fourier-Transform

Chaehwan Hwang(황채환),Suyeol Kim(김수열),Deokwoo Lee(이덕우) 한국산학기술학회 2019 한국산학기술학회논문지 Vol.20 No.7

최근 비침투 또는 비접촉 방식을 활용한 호흡상태 관찰에 대한 관심이 높아지고 있다. 여러 가지 많은 생체신호들중 호흡신호를 활용하여 건강상태를 점검하는 것은 비정상적인 건강 상태에 대한 신속한 대응을 가능하게 해 준다. 본 논문에서는 국소 퓨리에 변환을 활용한 실시간 무호흡 상태 검출에 대한 방법을 제시한다. 기존의 고속 퓨리에 변환을 활용한 신호해석과 달리, 본 논문에서는 국소 퓨리에 변환을 사용하여 짧은 신호 구간에서의 주파수 응답을 분석한다. 본 연구에서 호흡 신호는 비접촉 방식을 활용하였으며, 초광대역 레이더 모듈을 활용하여 신호를 획득하였다. 국소 퓨리에 변환을 활용하여 호흡 상태를 검출한 후, 검출 결과에 따라 호흡 상태에 대한 분류가 가능하다. 특히 국소 퓨리에 변환은 실시간으로 호흡 상태에 대한 주파수 분석이 가능하도록 하였다. 호흡신호에 잡음이 존재할 경우를 대비하여 적절한 필터링 알고리즘이 적용되었다. 본 논문에서 제안하는 방법은 직관적으로 구현이 가능하고, 실질적으로 사람의 호흡상태에 대한 분석이 가능하도록 해준다. 제안한 방법을 검증하기 위해 호흡신호를 활용한 실험결과를 제시한다. Recently, monitoring respiration of people has been of interest using non-invasive method. Among the vital signals usually used for indicating health status, non-invasive and portable device based monitoring respiratory status is practically useful and enable one to promptly deal with abnormal physical status. This paper proposes the approach to real-time detection of apnea signal based on Short-Time-Fourier-Transform(STFT). Contrary to the analysis of a signal in frequency domain using Fast-Fourier Transform, this paper employs Short-time-Fourier-Transform so that frequency response can be analyzed in short time interval. The respiratory signal is acquired using UWB radar sensor that enables one to obtain respiration signal in contactless way. Detection of respiratory status is carried out by analyzing frequency response, and classification of respiratory status can be provided. In particular, STFT is employed to analyze respiratory signal in real-time, leading to effective analysis of the respiratory status in practice. In the case of existence of noise in the signal, appropriate filtering process is employed as well. The proposed method is straightforward and is workable in practice to analyze the respiratory status of people. To evaluate the proposed method, experimental results are provided.

STFT 기법을 적용한 스펙트럼 센싱 모듈 구현

이현소(Hyun-So Lee),강민규(Min-Kyu Kang),문기탁(Ki-Tak Moon),김경석(Kyung-Seok Kim) 한국콘텐츠학회 2010 한국콘텐츠학회논문지 Vol.10 No.1

스펙트럼 센싱 기술은 차세대 무선통신 기술들 중 하나인 Cognitive Radio (CR) 시스템에서의 핵심기술이다. 본 논문은 CR 기술 기반의 효율적인 스펙트럼 센싱을 위하여 대상 신호의 시간-주파수 측면의 분석을 위한 알고리즘인 Short Time Fourier Transform을 적용하는 방법을 제안하였다. STFT에 적용된 윈도우는 Kaiser Window이며, 그 중첩 정도는 50%로 규정하였다. 시뮬레이션을 위해 6㎒ 대역폭을 가진 DVB-H 신호를 입력 신호로 하였으며, Modified Periodogram Method, Welch's Method와의 비교를 통하여 제안한 알고리즘의 성능을 확인하였다. 또한, 임베디드 보드를 통하여 스펙트럼 센싱 모듈을 구현하였다. The Spectrum Sensing Technology is the core technology of the Cognitive Radio (CR) System that is one of the future wireless communication technologies. In this paper, we proposed the efficient Spectrum Sensing Method using the Short Time Fourier Transform (STFT) that is the algorithm for Time-Frequency analysis of the raw data. Applied window function to STFT algorithm is a Kaiser window, it is piled up its 50% range. For the simulation, the DVB-H signal with the 6㎒ bandwidth is used as the Input Signal. And we confirm the Spectrum Sensing result using Modified Periodogram Method, Welch's Method for compared with Short Time Fourier Transform Algorithm. And also, Spectrum Sensing Module is implemented using embedded board.

방적사의 불규제 해석에 관한 연구(I) -Short-Time Fourier Transform을 이용한 분석-

정성훈 한국섬유공학회 2000 한국섬유공학회지 Vol.37 No.10

A new evenness evaluation method was developed in order to analyze short-termirregularity of spun yarns. The diameters of a spun yarn were measured at every 2 mm segments by using the G-580\ulcorner tester produced by Zweigle Ltd. Simultaneously, the analog signals from the instrument were captured and then converted into the digital signals with the digital signal processing system specially developed for this study by using Lab-PC+\ulcorner. In order to analyze the periodicity of the signals, Short-Time Fourier Transform(STFT) and Discrete Fourier Transform(DFT) were applied. The results from STFT were compared with those from DFT. It was found that STFT was more effective in analyzing the short-term irregularity of spun yarns than DFT and in detecting frequency content changes with time.

이산 웨이블릿 변환을 이용한 시계열 분류

임세은(Se Eun Lim),나종화(Jonghwa Na) 한국데이터정보과학회 2021 한국데이터정보과학회지 Vol.32 No.5

최근 각종 디지털 기기로부터 폭발적으로 생산되고 있는 시계열 자료에 대한 데이터마이닝 기법의 적용이 주요 관심사이다. 여기에는 시계열 자료에 대한 분류의 문제가 포함된다. 본 연구에서는 시계열 자료의 분류 문제에 이산 웨이블릿 변환을 이용하는 방법을 소개하고, 실제의 자료 분석에 적용한다. 이산 웨이블릿 변환은 시간의 정보를 담지 못하는 푸리에 분석의 발전된 형태로 시간 해상도와 주파수 해상도를 동시에 높일 수 있는 장점이 있어 비정상적 시계열 자료에 대한 분류 문제에 효과적이다. 분류 모형으로는 의사결정나무, 단순 베이즈, k-인접아웃, SVM, 랜덤 포레스트 방법이 사용되었으며, 성능 비교를 위해 정확도, 카파계수, F1-점수, ARI 등의 다양한 측도가 사용되었다. 염산의 농도를 측정한 시계열 자료에 적용한 결과, 이산 웨이블릿을 이용한 방법이 원시 시계열 자료에 직접 분류 모형을 적용한 결과나 고속 푸리에 변환 (또는 단시간 푸리에 변환)을 이용한 결과보다 성능이 우수함을 확인하였다. 이산 웨이블릿 변환의 적용과정에서 주요하게 취급되는 모 웨이블릿의 선택 문제도 함께 고려하였다. The application of data mining techniques to time-series data, which are being explosively produced from various digital devices, is a major concern. This includes the problem of classification for time series data. In this study, the method of using the discrete wavelet transform is introduced to the classification problem of time series data and applied to actual data analysis. Discrete wavelet transform is an advanced form of Fourier analysis that does not contain time information, and has the advantage of simultaneously increasing temporal resolution and frequency resolution, so it is effective in the classification problem of non-stationary time series data. Decision tree, naive Bayesian, k-NN, SVM, and random forest methods were used as classification models. And various measures, which include accuracy, kappa coefficient, F1-score, ARI, were used for performance comparison. As a result of applying these methods to time series data observing concentration of hydrochloric acid, it was confirmed that the method using discrete wavelets analysis performed better than the result of applying the classification model directly to the raw data or the result using the Fourier transform (or short time Fourier transform). The problem of selecting a mother wavelet, which is mainly handled in the process of applying the discrete wavelet transform, is also considered.

유도 초음파 신호 분석을 위한 적응 단시간 푸리에 변환

홍진철(Hong, Jin-Chul),선경호(Sun, Kyung-Ho),김윤영(Kim, Yoon-Young) 한국소음진동공학회 2005 한국소음진동공학회 논문집 Vol.15 No.3

Although time-frequency analysis is useful for dispersive wave analysis, conventional methods such as the short-time Fourier transform do not take the dispersion phenomenon into consideration in the tiling of the time-frequency domain. The objective of this paper is to develop an adaptive time-frequency analysis method whose time-frequency tiling is determined with the consideration of signal dispersion characteristics. To achieve the adaptive time-frequency tiling, each of time-frequency atoms is rotated in the time-frequency plane depending on the local wave dispersion. To carry out this adaptive time-frequency transform, dispersion characteristics hidden in a signal are first estimated by an iterative scheme. To examine the effectiveness of the present method, the flexural wave signals measured in a plate were analyzed.

단시간 푸리에 변환을 이용한 자동 음원 추출 연구

이정민,이원구 제어로봇시스템학회 2021 제어로봇시스템학회 국내학술대회 논문집 Vol.2021 No.6

Automatic music transcription is the process that convert a musical signal into the form of tablature. There are lots of researches related to automatic music transcription, however, studies on the transcription of bass guitar that provide bass tablature that contain detailed information such as length of note still remain few. Here, we report a method for extracting a bass guitar line from polyphonic music via short time Fourier transform. Briefly, we divided the target music waveform into individual sections to get the bass notes. The combination of fast Fourier transform(FFT) and short time Fourier transform was used to obtain each section’s frequency and the length of notes(or beats) of the target music. We also used the Euclidean method and minimum distance estimation to generate user-friendly tablature, leading to the least movement of player’s fingers. We believe that this approach can help contribute to the expansion of the K(orea)-music market by providing automated music transcriptions for worldwide amateurs who want to perform their favorite songs.

Advanced signal processing for enhanced damage detection with piezoelectric wafer active sensors

Yu, Lingyu,Giurgiutiu, Victor Techno-Press 2005 Smart Structures and Systems, An International Jou Vol.1 No.2

Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.